Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Large-scale citizen science reveals predictors of sensorimotor adaptation

Nature Human Behaviour volume 8, pages 510–525 (2024)Cite this article

Subjects

Abstract

Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Web-based sensorimotor adaptation task and behaviour.
Fig. 2: The effects of session and age on sensorimotor adaptation.
Fig. 3: Features of the early adaptation, late adaptation and aftereffect phases.
Fig. 4: Five patterns of data simulated by the competition theory.
Fig. 5: Features corresponding to Pattern 1.
Fig. 6: Features corresponding to Pattern 3.
Fig. 7: Features corresponding to Pattern 4.

Similar content being viewed by others

Data availability

The data are available at https://osf.io/5n7jf/.

Code availability

The analysis code is available at https://osf.io/5n7jf/.

References

  1. Krakauer, J., Hadjiosif, A. M., Xu, J., Wong, A. L. & Haith, A. M. Motor learning. Compr. Physiol. 9, 613–663 (2019).

    Article  PubMed  Google Scholar 

  2. Roemmich, R. T. & Bastian, A. J. Closing the loop: from motor neuroscience to neurorehabilitation. Annu. Rev. Neurosci. 41, 415–429 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Tsay, J. S. & Winstein, C. J. Five features to look for in early-phase clinical intervention studies. Neurorehabil. Neural Repair 35, 3–9 (2021).

    Article  PubMed  Google Scholar 

  4. Helmholtz, H. L. F. V. Treatise on Physiological Optics (Dover, 1924).

  5. Stratton, G. M. Some preliminary experiments on vision without inversion of the retinal image. Psychol. Rev. 3, 611–617 (1896).

    Article  Google Scholar 

  6. Ghilardi, M. et al. Patterns of regional brain activation associated with different forms of motor learning. Brain Res. 871, 127–145 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Krakauer, J., Pine, Z. M., Ghilardi, M. F. & Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20, 8916–8924 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Krakauer, J., Ghez, C. & Ghilardi, M. F. Adaptation to visuomotor transformations: consolidation, interference, and forgetting. J. Neurosci. 25, 473–478 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ferrea, E., Franke, J., Morel, P. et al. Statistical determinants of visuomotor adaptation along different dimensions during naturalistic 3D reaches. Sci. Rep. 12, 10198 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kagerer, F. A., Contreras-Vidal, J. L. & Stelmach, G. E. Adaptation to gradual as compared with sudden visuo-motor distortions. Exp. Brain Res. 115, 557–561 (1997).

    Article  CAS  PubMed  Google Scholar 

  11. Shadmehr, R., Smith, M. A. & Krakauer, J. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Kim, H. E., Avraham, G. & Ivry, R. B. The psychology of reaching: action selection, movement implementation, and sensorimotor learning. Annu. Rev. Psychol. https://doi.org/10.1146/annurev-psych-010419-051053 (2020).

  13. McDougle, S. D., Ivry, R. B. & Taylor, J. A. Taking aim at the cognitive side of learning in sensorimotor adaptation tasks. Trends Cogn. Sci. 20, 535–544 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hegele, M. & Heuer, H. Implicit and explicit components of dual adaptation to visuomotor rotations. Conscious. Cogn. 19, 906–917 (2010).

    Article  PubMed  Google Scholar 

  15. Benson, B. L., Anguera, J. A. & Seidler, R. D. A spatial explicit strategy reduces error but interferes with sensorimotor adaptation. J. Neurophysiol. 105, 2843–2851 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Redding, G. M. & Wallace, B. Adaptive spatial alignment and strategic perceptual–motor control. J. Exp. Psychol. Hum. Percept. Perform. 22, 379–394 (1996).

    Article  CAS  PubMed  Google Scholar 

  17. Tsay, J. S. et al. Strategic processes in sensorimotor learning: reasoning, refinement, and retrieval. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/x4652 (2023).

  18. Tsay, J. S. et al. The effect of visual uncertainty on implicit motor adaptation. J. Neurophysiol. https://doi.org/10.1152/jn.00493.2020 (2021).

  19. Kim, H. E., Morehead, R., Parvin, D. E., Moazzezi, R. & Ivry, R. B. Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity. Commun. Biol. 1, 19 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Herzfeld, D. J., Vaswani, P. A., Marko, M. K. & Shadmehr, R. A memory of errors in sensorimotor learning. Science 345, 1349–1353 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Albert, S. T. et al. An implicit memory of errors limits human sensorimotor adaptation. Nat. Hum. Behav. https://doi.org/10.1038/s41562-020-01036-x (2021).

  22. Redding, G. M. & Wallace, B. Effects on prism adaptation of duration and timing of visual feedback during pointing. J. Mot. Behav. 22, 209–224 (1990).

    Article  CAS  PubMed  Google Scholar 

  23. Held, R., Efstathiou, A. & Greene, M. Adaptation to displaced and delayed visual feedback from the hand. J. Exp. Psychol. 72, 887–891 (1966).

    Article  Google Scholar 

  24. Kitazawa, S., Kohno, T. & Uka, T. Effects of delayed visual information on the rate and amount of prism adaptation in the human. J. Neurosci. 15, 7644–7652 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Brudner, S. N., Kethidi, N., Graeupner, D., Ivry, R. B. & Taylor, J. A. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use. J. Neurophysiol. 115, 1499–1511 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tsay, J. S., Irving, C. & Ivry, R. B. Signatures of contextual interference in implicit sensorimotor adaptation. Proc. Biol. Sci. 290, 20222491 (2023).

    PubMed  PubMed Central  Google Scholar 

  27. Tsay, J. S., Kim, H., Haith, A. M., & Ivry, R. B. Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment. eLife https://doi.org/10.7554/eLife.76639 (2022).

  28. Martin, T. A., Keating, J. G., Goodkin, H. P., Bastian, A. J. & Thach, W. T. Throwing while looking through prisms: I. Focal olivocerebellar lesions impair adaptation. Brain 119, 1183–1198 (1996).

    Article  PubMed  Google Scholar 

  29. Tzvi, E., Loens, S. & Donchin, O. Mini-review: the role of the cerebellum in visuomotor adaptation. Cerebellum https://doi.org/10.1007/s12311-021-01281-4 (2021).

  30. Tsay, J. S., Najafi, T., Schuck, L., Wang, T. & Ivry, R. B. Implicit sensorimotor adaptation is preserved in Parkinson’s disease. Brain Commun. 4, fcac303 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tsay, J. S., Schuck, L., & Ivry, R. B. Cerebellar degeneration impairs strategy discovery but not strategy recall. Cerebellum https://doi.org/10.1007/s12311-022-01500-6 (2022).

  32. Mutha, P. K., Sainburg, R. L. & Haaland, K. Y. Left parietal regions are critical for adaptive visuomotor control. J. Neurosci. 31, 6972–6981 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Smith, M. A. & Shadmehr, R. Intact ability to learn internal models of arm dynamics in Huntington’s disease but not cerebellar degeneration. J. Neurophysiol. 93, 2809–2821 (2005).

    Article  PubMed  Google Scholar 

  34. Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010). Discussion 83–135.

    Article  PubMed  Google Scholar 

  35. Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 1100–1122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wang, X., Abdullah, B. & Samsudin, S. The effect of contextual interference on motor learning among healthy adolescents: a systematic review. J. Posit. Sch. Psychol. 6, 4545–4580 (2022).

    Google Scholar 

  37. Shewokis, P. A. Is the contextual interference effect generalizable to computer games? Percept. Mot. Skills 84, 3–15 (1997).

    Article  CAS  PubMed  Google Scholar 

  38. Kantner, L. A., Segall, M. H., Campbell, D. T. & Herskovits, M. J. The influence of culture on visual perception. Stud. Art. Educ. 10, 68–71 (1968).

    Article  Google Scholar 

  39. Pitt, B., Carstensen, A., Boni, I., Piantadosi, S. T. & Gibson, E. Different reference frames on different axes: space and language in indigenous Amazonians. Sci. Adv. 8, eabp9814 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Anderson, D. I., Lohse, K. R., Lopes, T. C. V. & Williams, A. M. Individual differences in motor skill learning: past, present and future. Hum. Mov. Sci. 78, 102818 (2021).

    Article  PubMed  Google Scholar 

  41. Seidler, R. D. & Carson, R. G. Sensorimotor learning: neurocognitive mechanisms and individual differences. J. Neuroeng. Rehabil. https://doi.org/10.1186/s12984-017-0279-1 (2017).

  42. Ranganathan, R., Cone, S. & Fox, B. Predicting individual differences in motor learning: a critical review. Neurosci. Biobehav. Rev. 141, 104852 (2022).

    Article  PubMed  Google Scholar 

  43. Ackerman, P. L. Determinants of individual differences during skill acquisition: cognitive abilities and information processing. J. Exp. Psychol. Gen. 117, 288–318 (1988).

    Article  Google Scholar 

  44. Fleishman, E. A. On the relation between abilities, learning, and human performance. Am. Psychol. 27, 1017–1032 (1972).

    Article  Google Scholar 

  45. Tsay, J. S., Lee, A., Ivry, R. B., & Avraham, G. Moving outside the lab: the viability of conducting sensorimotor learning studies online. Neurons Behav. Data Anal. https://doi.org/10.51628/001c.26985 (2021).

  46. Tsay, J. S. et al. OnPoint: a package for online experiments in motor control and motor learning. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/hwmpy (2020).

  47. Germine, L. et al. Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments. Psychon. Bull. Rev. 19, 847–857 (2012).

    Article  PubMed  Google Scholar 

  48. Germine, L. T., Duchaine, B. & Nakayama, K. Where cognitive development and aging meet: face learning ability peaks after age 30. Cognition 118, 201–210 (2011).

    Article  PubMed  Google Scholar 

  49. Wilmer, J. B. et al. Capturing specific abilities as a window into human individuality: the example of face recognition. Cogn. Neuropsychol. 29, 360–392 (2012).

    Article  PubMed  Google Scholar 

  50. Kim, H. et al. Multiracial Reading the Mind in the Eyes Test (MRMET): an inclusive version of an influential measure. Preprint at OSF https://doi.org/10.31219/osf.io/y8djm (2022).

  51. Wilmer, J. B. How to use individual differences to isolate functional organization, biology, and utility of visual functions; with illustrative proposals for stereopsis. Spat. Vis. 21, 561–579 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bond, K. & Taylor, J. A. Flexible explicit but rigid implicit learning in a visuomotor adaptation task. J. Neurophysiol. 113, 3836–3849 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Shyr, M. C. & Joshi, S. S. A case study of the validity of web-based visuomotor rotation experiments. J. Cogn. Neurosci. 36, 71–94 (2024).

    Article  PubMed  Google Scholar 

  54. Kim, O. A., Forrence, A. D. & McDougle, S. D. Motor learning without movement. Proc. Natl Acad. Sci. USA 119, e2204379119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34, 3023–3032 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Anwyl-Irvine, A., Dalmaijer, E.S., Hodges, N. et al. Realistic precision and accuracy of online experiment platforms, web browsers, and devices. Behav. Res. 53, 1407–1425 (2021).

    Article  Google Scholar 

  57. Flanagan, J. C. A simplified procedure for determining the reliability of a test by split-halves. J. Educ. Psychol. 28, 99–103 (1937).

    Google Scholar 

  58. Allen, M. J. Introduction to Measurement Theory (Waveland, 1979).

  59. Avraham, G., Morehead, R., Kim, H. E. & Ivry, R. B. Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. PLoS Biol. 19, e3001147 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Tsay, J. S., Kim, H. E., Parvin, D. E., Stover, A. R. & Ivry, R. B. Individual differences in proprioception predict the extent of implicit sensorimotor adaptation. J. Neurophysiol. https://doi.org/10.1152/jn.00585.2020 (2021).

  61. Huberdeau, D. M., Krakauer, J. W. & Haith, A. M. Practice induces a qualitative change in the memory representation for visuomotor learning. J. Neurophysiol. https://doi.org/10.1152/jn.00830.2018 (2019).

  62. Haith, A. M., Huberdeau, D. M. & Krakauer, J. W. The influence of movement preparation time on the expression of visuomotor learning and savings. J. Neurosci. 35, 5109–5117 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Morehead, R., Qasim, S. E., Crossley, M. J. & Ivry, R. Savings upon re-aiming in visuomotor adaptation. J. Neurosci. 35, 14386–14396 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Schmitz, G. Enhanced cognitive performance after multiple adaptations to visuomotor transformations. PLoS ONE 17, e0274759 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tsay, J. S., Irving, C. & Ivry, R. B. Signatures of contextual interference in implicit sensorimotor adaptation. Proc. R. Soc. B 290, 20222491 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Shea, J. B. & Morgan, R. L. Contextual interference effects on the acquisition, retention, and transfer of a motor skill. J. Exp. Psychol. Hum. Learn. 5, 179–187 (1979).

    Article  Google Scholar 

  67. Hadjiosif, A. M. & Smith, M. A. A double dissociation between savings and long-term memory in motor learning. PLoS Biol. 21, e3001799 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hadjiosif, A. M., Morehead, J. R. & Smith, M. A. A double dissociation between savings and long-term memory in motor learning. PLoS Biol. 21, e3001799 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Joiner, W. M. & Smith, M. A. Long-term retention explained by a model of short-term learning in the adaptive control of reaching. J. Neurophysiol. 100, 2948–2955 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Miyamoto, Y. R., Wang, S. & Smith, M. A. Implicit adaptation compensates for erratic explicit strategy in human motor learning. Nat. Neurosci. 23, 443–455 (2020).

    Article  CAS  PubMed  Google Scholar 

  71. Roller, C. A., Cohen, H. S., Kimball, K. T. & Bloomberg, J. J. Effects of normal aging on visuo-motor plasticity. Neurobiol. Aging 23, 117–123 (2002).

    Article  PubMed  Google Scholar 

  72. Buch, E. R., Young, S. & Contreras-Vidal, J. L. Visuomotor adaptation in normal aging. Learn. Mem. 10, 55–63 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Vachon, C. M., Modchalingam, S., ’t Hart, B. M. & Henriques, D. Y. P. The effect of age on visuomotor learning processes. PLoS ONE 15, e0239032 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Wolpe, N. et al. Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory. Neurobiol. Aging https://doi.org/10.1016/j.neurobiolaging.2020.02.016 (2020).

  75. Wang, T. S. L., Martinez, M., Festa, E. K., Heindel, W. C. & Song, J.-H. Age-related enhancement in visuomotor learning by a dual-task. Sci. Rep. 12, 5679 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cressman, E. K., Salomonczyk, D. & Henriques, D. Y. P. Visuomotor adaptation and proprioceptive recalibration in older adults. Exp. Brain Res. 205, 533–544 (2010).

    Article  PubMed  Google Scholar 

  77. Vandevoorde, K. & Orban de Xivry, J.-J. Why is the explicit component of motor adaptation limited in elderly adults? J. Neurophysiol. 124, 152–167 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Wong, A. L., Marvel, C. L., Taylor, J. A. & Krakauer, J. W. Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits? Brain https://doi.org/10.1093/brain/awy334 (2019).

  79. Seidler, R. D. Differential effects of age on sequence learning and sensorimotor adaptation. Brain Res. Bull. 70, 337–346 (2006).

    Article  PubMed  Google Scholar 

  80. Ruitenberg, M. F. L., Koppelmans, V., Seidler, R. D. & Schomaker, J. Developmental and age differences in visuomotor adaptation across the lifespan. Psychol. Res. https://doi.org/10.1007/s00426-022-01784-7 (2023).

  81. Vandevoorde, K. & Orban de Xivry, J.-J. Internal model recalibration does not deteriorate with age while motor adaptation does. Neurobiol. Aging 80, 138–153 (2019).

    Article  PubMed  Google Scholar 

  82. Morehead, R. & de Xivry, J.-J. O. A synthesis of the many errors and learning processes of visuomotor adaptation. Preprint at bioRxiv https://doi.org/10.1101/2021.03.14.435278 (2021).

  83. Verstynen, T. & Kording, K. P. Overfitting to ‘predict’ suicidal ideation. Nat. Hum. Behav. 7, 680–681 (2023).

    Article  PubMed  Google Scholar 

  84. Albert, S. T. et al. Competition between parallel sensorimotor learning systems. eLife https://doi.org/10.7554/eLife.65361 (2022).

  85. Tottenham, L. S. & Saucier, D. M. Throwing accuracy during prism adaptation: male advantage for throwing accuracy is independent of prism adaptation rate. Percept. Mot. Skills 98, 1449–1455 (2004).

    Article  PubMed  Google Scholar 

  86. Zar, J. H. Biostatistical Analysis: International Edition 5th edn (Pearson, 2007).

  87. Gajda, K., Sülzenbrück, S. & Heuer, H. Financial incentives enhance adaptation to a sensorimotor transformation. Exp. Brain Res. 234, 2859–2868 (2016).

    Article  PubMed  Google Scholar 

  88. Tsay, J. S., Tan, S., Chu, M., Ivry, R. B. & Cooper, E. A. Low vision impairs implicit sensorimotor adaptation in response to small errors, but not large errors. J. Cogn. Neurosci. https://doi.org/10.1162/jocn_a_01969 (2023).

  89. Burge, J., Ernst, M. O. & Banks, M. S. The statistical determinants of adaptation rate in human reaching. J. Vis. 8, 20 (2008).

    Article  Google Scholar 

  90. Körding, K. P. & Wolpert, D. M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004).

    Article  PubMed  Google Scholar 

  91. McDougle, S. D. & Taylor, J. A. Dissociable cognitive strategies for sensorimotor learning. Nat. Commun. 10, 40 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Fernandez-Ruiz, J., Wong, W., Armstrong, I. T. & Flanagan, J. R. Relation between reaction time and reach errors during visuomotor adaptation. Behav. Brain Res. 219, 8–14 (2011).

    Article  PubMed  Google Scholar 

  93. Morehead, J. R. & Ivry, R. Intrinsic Biases Systematically Affect Visuomotor Adaptation Experiments (Society for Neural Control of Movement, 2015); http://ivrylab.berkeley.edu/uploads/4/1/1/5/41152143/morehead_ncm2015.pdf

  94. Vindras, P., Desmurget, M., Prablanc, C. & Viviani, P. Pointing errors reflect biases in the perception of the initial hand position. J. Neurophysiol. 79, 3290–3294 (1998).

    Article  CAS  PubMed  Google Scholar 

  95. Wilson, E. T., Wong, J. & Gribble, P. L. Mapping proprioception across a 2D horizontal workspace. PLoS ONE 5, e11851 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  96. McNay, E. C. & Willingham, D. B. Deficit in learning of a motor skill requiring strategy, but not of perceptuomotor recalibration, with aging. Learn. Mem. 4, 411–420 (1998).

    Article  CAS  PubMed  Google Scholar 

  97. Fernández-Ruiz, J., Hall, C., Vergara, P. & Díiaz, R. Prism adaptation in normal aging: slower adaptation rate and larger aftereffect. Brain Res. Cogn. Brain Res. 9, 223–226 (2000).

    Article  PubMed  Google Scholar 

  98. Dhawale, A. K., Smith, M. A. & Ölveczky, B. P. The role of variability in motor learning. Annu. Rev. Neurosci. 40, 479–498 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. He, K. et al. The statistical determinants of the speed of motor learning. PLoS Comput. Biol. 12, e1005023 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Wu, H. G., Miyamoto, Y. R., Castro, L. N. G., Ölveczky, B. P. & Smith, M. A. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat. Neurosci. 17, 312–321 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Singh, P., Jana, S., Ghosal, A. & Murthy, A. Exploration of joint redundancy but not task space variability facilitates supervised motor learning. Proc. Natl Acad. Sci. USA 113, 14414–14419 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).

    Article  CAS  PubMed  Google Scholar 

  103. Tsay, J. S., Kim, H., Haith, A. M. & Ivry, R. B. Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment. eLife https://doi.org/10.7554/eLife.76639 (2022).

  104. Bönstrup, M., Iturrate, I., Hebart, M. N., Censor, N. & Cohen, L. G. Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. NPJ Sci. Learn. 5, 7 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Taylor, J. A. & Ivry, R. B. Flexible cognitive strategies during motor learning. PLoS Comput. Biol. 7, e1001096 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Hebiri, M. & Lederer, J. How correlations influence lasso prediction. IEEE Trans. Inf. Theory 59, 1846–1854 (2013).

    Article  Google Scholar 

  107. Burgoyne, A. P., Harris, L. J. & Hambrick, D. Z. Predicting piano skill acquisition in beginners: the role of general intelligence, music aptitude, and mindset. Intelligence 76, 101383 (2019).

    Article  Google Scholar 

  108. McGregor, H. R. & Gribble, P. L. Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing. J. Neurophysiol. 118, 1235–1243 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Roberts, R. E., Bain, P. G., Day, B. L. & Husain, M. Individual differences in expert motor coordination associated with white matter microstructure in the cerebellum. Cereb. Cortex 23, 2282–2292 (2013).

    Article  CAS  PubMed  Google Scholar 

  110. Landi, S. M., Baguear, F. & Della-Maggiore, V. One week of motor adaptation induces structural changes in primary motor cortex that predict long-term memory one year later. J. Neurosci. 31, 11808–11813 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Koppelmans, V., Bloomberg, J. J., Mulavara, A. P. & Seidler, R. D. Brain structural plasticity with spaceflight. NPJ Microgravity https://doi.org/10.1038/s41526-016-0001-9 (2016).

  112. Pearson-Fuhrhop, K. M., Minton, B., Acevedo, D., Shahbaba, B. & Cramer, S. C. Genetic variation in the human brain dopamine system influences motor learning and its modulation by l-DOPA. PLoS ONE 8, e61197 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Listman, J. B., Tsay, J. S., Kim, H. E., Mackey, W. E. & Heeger, D. J. Long-term motor learning in the ‘wild’ with high volume video game data. Front. Hum. Neurosci. 15, 777779 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Aung, M. et al. Predicting skill learning in a large, longitudinal MOBA dataset. In IEEE Conference on Computational Intelligence and Games (CIG) 1–7 (IEEE, 2018).

  115. Brookes, J., Warburton, M., Alghadier, M., Mon-Williams, M. & Mushtaq, F. Studying human behavior with virtual reality: the Unity Experiment Framework. Behav. Res Methods 52, 455–463 (2020).

    Article  PubMed  Google Scholar 

  116. Chen, X. et al. Age-dependent Pavlovian biases influence motor decision-making. PLoS Comput. Biol. 14, e1006304 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Donovan, I., Saul, M. A., DeSimone, K., Listman, J. B., Mackey, W. E., & Heeger, D. J. Assessment of human expertise and movement kinematics in first-person shooter games. Front. Hum. Neurosci. https://doi.org/10.3389/fnhum.2022.979293 (2022).

  118. Stafford, T. & Dewar, M. Tracing the trajectory of skill learning with a very large sample of online game players. Psychol. Sci. 25, 511–518 (2014).

    Article  PubMed  Google Scholar 

  119. Stafford, T., & Vaci, N. Maximizing the potential of digital games for understanding skill acquisition. Curr. Dir. Psychol. https://doi.org/10.1177/09637214211057841 (2022).

  120. Balestrucci, P., Wiebusch, D. & Ernst, M. O. ReActLab: a custom framework for sensorimotor experiments ‘in-the-wild’. Front. Psychol. https://doi.org/10.3389/fpsyg.2022.906643 (2022).

  121. Kaur, J. & Balasubramaniam, R. Sequence learning in an online serial reaction time task: the effect of task instructions. J. Mot. Learn. Dev. 1–17 (2022).

  122. Brantley, J. A. & Kording, K. P. Bayesball: Bayesian integration in professional baseball batters. Preprint at bioRxiv https://doi.org/10.1101/2022.10.12.511934 (2022).

  123. Drazan, J. F., Phillips, W. T., Seethapathi, N., Hullfish, T. J. & Baxter, J. R. Moving outside the lab: markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump. J. Biomech. 125, 110547 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Hausmann, S. B., Vargas, A. M., Mathis, A. & Mathis, M. W. Measuring and modeling the motor system with machine learning. Preprint at arXiv https://doi.org/10.48550/arXiv.2103.11775 (2021).

  125. Hooyman, A. & Schaefer, S. Y. Age and sex effects on Super G performance are consistent across internet devices. Int. J. Serious Games 10, 25–36 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Yin, C. & Wei, K. Savings in sensorimotor adaptation without explicit strategy. J. Neurophysiol. https://doi.org/10.1152/jn.00524.2019 (2020).

  127. Coltman, S. K., Cashaback, J. G. A. & Gribble, P. L. Both fast and slow learning processes contribute to savings following sensorimotor adaptation. J. Neurophysiol. 121, 1575–1583 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Morehead, R., Taylor, J. A., Parvin, D. E. & Ivry, R. B. Characteristics of implicit sensorimotor adaptation revealed by task-irrelevant clamped feedback. J. Cogn. Neurosci. 29, 1061–1074 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Maresch, J., Werner, S. & Donchin, O. Methods matter: your measures of explicit and implicit processes in visuomotor adaptation affect your results. Eur. J. Neurosci. https://doi.org/10.1111/ejn.14945 (2020).

  130. Hooyman, A., Huentelman, M. J., De Both, M., Ryan, L. & Schaefer, S. Y. Establishing the validity and reliability of an online motor learning game: applications for Alzheimer’s disease research within MindCrowd. Games Health J. https://doi.org/10.1089/g4h.2022.0042 (2023).

  131. Allen, K. R., Smith, K. A. & Tenenbaum, J. B. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proc. Natl Acad. Sci. USA 117, 29302–29310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Tsay, J. S., Schuck, L. & Ivry, R. B. Cerebellar degeneration impairs strategy discovery but not strategy recall. Cerebellum https://doi.org/10.1007/s12311-022-01500-6 (2022).

  133. Saban, W. & Ivry, R. B. PONT: a Protocol for Online Neuropsychological Testing. J. Cogn. Neurosci. 1–13 (2021).

  134. Chakraborty, S. & Wong, S. W. K. BAMBI: An R package for fitting bivariate angular mixture models. J. Stat. Softw. 99, 1–69 (2021).

    Article  Google Scholar 

  135. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Choi, Y., Park, R. & Seo, M. Lasso on Categorical Data (CiteSeerX, 2012); https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.278.5439

  137. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).

    Google Scholar 

  138. McDougle, S. D., Bond, K. & Taylor, J. A. Implications of plan-based generalization in sensorimotor adaptation. J. Neurophysiol. 118, 383–393 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Day, K. A., Roemmich, R. T., Taylor, J. A. & Bastian, A. J. Visuomotor learning generalizes around the intended movement. eNeuro https://doi.org/10.1523/ENEURO.0005-16.2016 (2016).

Download references

Acknowledgements

This project was supported by two NIH grants (no. 1F31NS120448 awarded to J.S.T. and no. R35NS116883-01 awarded to R.B.I.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Jonathan S. Tsay, Hrach Asmerian.

Authors and Affiliations

  1. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA

    Jonathan S. Tsay

  2. Department of Psychology, University of California, Berkeley, Berkeley, CA, USA

    Hrach Asmerian, Richard B. Ivry & Ken Nakayama

  3. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Laura T. Germine

  4. Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA

    Laura T. Germine

  5. Department of Psychology, Wellesley College, Wellesley, MA, USA

    Jeremy Wilmer

  6. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Richard B. Ivry

Authors
  1. Jonathan S. Tsay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hrach Asmerian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura T. Germine
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeremy Wilmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard B. Ivry
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ken Nakayama
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.S.T.: conceptualization, resources, data curation, software, formal analysis, funding acquisition, validation, investigation, visualization, methodology, project administration, writing—original draft, writing—review and editing. H.A.: software, formal analysis, validation, investigation, visualization, methodology, writing—review and editing. L.T.G.: data curation, visualization, writing—review and editing. J.W.: data curation, visualization, writing—review and editing. R.B.I.: data curation, writing—review and editing, funding acquisition, validation, investigation, visualization. K.N.: data curation, writing—review and editing, validation, investigation, visualization, project administration.

Corresponding authors

Correspondence to Jonathan S. Tsay or Hrach Asmerian.

Ethics declarations

Competing interests

R.B.I. is a co-founder with equity in Magnetic Tides, Inc., a biotechnology company created to develop a novel method of non-invasive brain stimulation. The other authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Joshua Cashaback, Scott T Albert, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Correlations between different phases of motor adaptation.

Correlation between early and late adaptation (a), aftereffect and early adaptation (b), and aftereffect and late adaptation (c). rp denotes Pearson’s correlation (# of sessions = 1,747); pbf denotes the p-value for a two-tailed t-test (Bonferroni-corrected for three comparisons).

Extended Data Fig. 2 Age distribution.

Blue shading denotes different age groups (that is, rounded to the nearest decade). 107 individuals are closest to age 10, 1068 to age 20, 269 to age 30, 126 to age 40, 61 to age 50, 59 to age 60, 24 to age 70, 28 to age 80, and 5 to age 90. The oldest group was excluded in our aging analyses due to its limited sample size (n = 5).

Extended Data Fig. 3 Correlation matrix.

Color denotes the direction of the Pearson’s correlations (# of sessions = 1,747), and square size denotes correlation magnitude.

Extended Data Fig. 4 Results from our post-hoc Lasso regression were robust to changes in number of folds and percent of data used for training.

a–c, Shading and numbers denote the cross-validated coefficient of determination (\({R}_{{cv}}^{2}\)). The red box denotes the model used in this manuscript (that is, 80% training data split across 10-folds).

Extended Data Fig. 5 Features corresponding to Pattern 3 (continued).

(a, b) Baseline reaction time, (c, d) average amount of sleep every night, and (e, f) computer screen size. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. rs denotes Spearman’s correlation; p value is obtained from a two-tailed t-test. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 6 Features corresponding to Pattern 5.

(a, b) Perturbation direction, (c, d) device used, (e, f) self-reported neurological disease, and (g, h) amount of average computer usage. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. rs denotes Spearman’s correlation; p value is obtained from a two-tailed t-test. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 7 Features corresponding to Pattern 5 (continued).

(a, b) Internet browser used, (c, d) undergraduate major, and (e, f) self-reported ratings of clumsiness. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 8 Self-reported neurological disease.

Among the 313 individuals reporting a neurological disease, only 12 described their specific disease, which we categorized into five main categories. AD: Alzheimer′s Disease. CD: Cerebellar Degeneration. MS: Multiple Sclerosis. PD: Parkinson’s Disease. ST: Stroke.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Table 1.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsay, J.S., Asmerian, H., Germine, L.T. et al. Large-scale citizen science reveals predictors of sensorimotor adaptation. Nat Hum Behav 8, 510–525 (2024). https://doi.org/10.1038/s41562-023-01798-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-023-01798-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing